A certain number of studies have therefore focused on the search for particular configurations at lower levels, mesoscopic or microscopic, to explain macroscopic observations.
For example the existence of different types of small world networks can be explained by a process that acts at the level of the node itself by limiting the number of links in the construction of the network [1]}.
Or the community structure commonly found in social networks could explain the degree assortativity [2]}.
The search for communities in networks has been a very active area of research and, for a given network, the statistical features of its communities is a signature of the network's belonging to one of five types among information, communication, technological, biological, or social networks [3]}. When the network hosts a dynamic process, and it is often the case for real world networks, it makes sense to analyze network topology through the prism of the dynamics that can take place.
Let us take a current topic, the spread of an epidemic. The existence of a community network structure influences the process of spreading an epidemic, but the internal structure of communities has little influence provided that they are dense enough in connections [4]}. Paradoxically, the way in which communities are articulated with each other has received relatively little attention from the community.
If we want to find papers that study the way in which mesoscopic structures are organised within a network, we should look instead for Core-Periphery analyses, with properties as varied as rich-club, nested, or onion configuration [5]}, and in particular analyses that allow us to identify several cores [6]}. More recently [7]} have proposed an algorithm specifically dedicated to the search for sets of nodes that articulate communities.[Read, 2016]
